Assessment and uncertainty estimation of weather-forecast driven data transfer for space exploration at Ka- and X-band

This paper aims at improving and assessing a previously developed technique to predict data volume transfer during deep-space satellite communication links at Ka- and $X$ -band (where the Earth's atmosphere affects propagating signals). The proposed technique exploits a weather forecast (WF) model to predict the atmospheric state and a radiative transfer model to convert the atmospheric state into radiopropagation variables. The latter are used to design the link budget and to maximize transferred data-volume. This WF-based technique exploits the atmospheric attenuation as a random variable related to the statistic of the transmission error rate that drives the received data-volume and its uncertainty. The WF-based technique is evaluated for the test case of the BepiColombo mission to Mercury from ESA (European space agency) considering Cebreros and Malargue receiving ground-stations. Tuning and verification of the adopted models were accomplished exploiting ground-based meteorological measurements (weather stations, radiosoundings, and microwave radiometer) and simulating four years of data transmission. Results, in terms of yearly received data-volume and its uncertainty, highlight the advantages of short-term WF-based atmospheric statistics in opposition to the commonly used long-term climatological statistics. These advantages are evaluated at both Ka- and $X$ -band. The use of aggregated statistics derived from WF data is demonstrated as a reliable possibility of bypassing the lack of meteorological measurements.